The Moral Limits of Predictive Practices: The Case of Credit-Based Insurance Scores

Barbara Kiviat is a Ph.D. candidate in Sociology and Social Policy at Harvard University. Her research explores how moral beliefs shape markets built on consumer data, and how those beliefs provide on-going justification for the economic inequalities that result. Her research has been supported by the National Science Foundation, the Washington Center for Equitable Growth, the Edmond J. Safra Center for Ethics, and Harvard’s Multidisciplinary Program in Inequality and Social Policy. Previously, she was a staff writer at Time magazine.

Title: The Moral Limits of Predictive Practices: The Case of Credit-Based Insurance Scores

Abstract: Corporations increasingly gather massive amounts of consumer data to predict how individuals will behave, so that they can more profitably price goods and allocate resources like insurance, credit, and jobs.

In this talk, I investigate the moral foundations of such market practices. I leverage the case of credit scores in car insurance pricing—an early and controversial use of algorithms in the U.S. consumer economy—to understand how mathematical prediction functions as a framework of market fairness and the ways people push back against it. Drawing on more than 6,000 pages of documents produced during public policy debates since the 1990s, I show that the mathematical mechanics of prediction complicate efforts to morally reason in nuanced ways; in this case, to determine if it really is fair to charge people with low credit scores more for car insurance.

Policymakers respond by searching for causal theories, the thing algorithmic prediction is supposed to eclipse, and re-sorting consumers from mathematical categories into categories of moral deservingness. This process, which I call de-commensuration, shapes the law and regulation that result. This research points to an important new direction for the study of markets and their impact on stratification in the era of “big data.”